A Situation Awareness Perspective on Human-AI Interaction: Tensions and Opportunities
Bibliographic record
Abstract
With the emergent focus on human-centered artificial intelligence (HCAI), research is required to understand the humanistic aspects of AI design, identify the mechanisms through which user concerns may be alleviated, thereby positively influencing AI adoption. To fill this void, we introduce “Situation Awareness” (SA) as a conceptual framework for considering human-AI interaction (HAII). We argue that SA is an appropriate and valuable theoretical lens through which to decompose and view HAII as hierarchical layers that allow for closer inquiry and discovery. Furthermore, we illustrate why the SA perspective is particularly relevant to the current need to understand HCAI by identifying three tensions inherent in AI design and explaining how an SA-oriented approach may help alleviate these tensions. We posit that users’ enactment of SA will mitigate some negative impacts of AI systems on user experience, improve human agency during AI system use, and promote more efficient and effective in-situ decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".